Predictive control method and system for a magnetic levitation system
A predictive control and magnetic levitation technology, applied in adaptive control, general control system, control/regulation system, etc., can solve problems such as difficulty in meeting real-time requirements, large online calculation amount, etc., to achieve no loss of accuracy and less online calculation amount Effect
Active Publication Date: 2021-01-08
CENT SOUTH UNIV
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Problems solved by technology
The biggest problem with the predictive model control algorithm is the large amount of online calculations and the difficulty in meeting real-time requirements. Therefore, the application of model predictive control is generally limited to slow motion, and there are certain limitations in the fast follow-up application of the magnetic levitation ball system.
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[0034]The structure of the magnetic levitation ball system of the present invention is attachedfigure 1As shown, this is a single degree of freedom system that can only control the up and down movement of the steel ball. No. 1 is steel ball, No. 2 is electromagnetic winding, No. 3 is LED light source, No. 4 is photoelectric board, No. 5 is processing circuit, No. 6 is electromagnetic winding drive circuit, No. 7 is A / D converter, and No. 8 is D / A converter, No. 9 is a PC computer. Work is called: PC output control voltage digital signal, converted by D / A converter, the analog signal is transmitted to the electromagnetic winding drive circuit, under the action of current, the electromagnetic winding generates an electromagnetic field around the winding through electromagnetic induction, thereby The steel ball in the magnetic field forms an electromagnetic induction force F, which makes the steel ball move upward. When the electromagnetic force F is equal to the gravity G of the steel...
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The invention discloses a prediction control method and system for a magnetic levitation system. By historical data of the magnetic levitation ball system and through adopting a system identificationmethod, an autoregressive model which takes a Gaussian radial basis function network as a coefficient and has an exogenous variable is established to describe nonlinear dynamic characteristics betweenan input voltage of an electromagnetic winding and a position of a steel ball. An ARX model coefficient is fit by an RBF neural network, so that an RBF-ARX model can well depict the nonlinear dynamiccharacteristics of the magnetic levitation ball system. Then, on the basis of a principle of a predictive functional control algorithm, a structure of a control input is determined so as to completerolling optimization and error correction. The predictive functional control algorithm has higher tracking capacity and higher robustness than PID control, and has a smaller online calculation amountand a higher control speed than conventional model prediction control; and when a system set value is a step signal, the control method can implement unbiased tracking.
Description
Technical field[0001]The invention relates to the technical field of automatic control, in particular to a control method based on an RBF-ARX model that uses a predictive function to control a magnetic levitation system.Background technique[0002]Magnetic levitation technology is a typical mechatronics technology integrating electromagnetics, electronic technology, control engineering, signal processing, mechanics, and dynamics. Magnetic levitation has the characteristics of low noise, low friction, high speed, etc., so that magnetic levitation has broad application and development potential in aerospace, railway transportation and motors. In order to improve the control performance of the magnetic levitation technology, the magnetic levitation ball system is often used as a main research platform. The magnetic levitation ball system mainly applies Newton’s second law to apply a certain current to the electromagnetic winding to generate electromagnetic force, which gives the magnetic...
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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 彭辉李雯李宏亮付雷方知涵
Owner CENT SOUTH UNIV